Sub-synchronous interactions (SSI) are a family of physical interactions that involve exchange of energy between a generator and a transmission system at ac frequencies below the system nominal frequency. They include sub-synchronous resonance (SSR), sub-synchronous torsional interaction (SSTI), and sub-synchronous control instability (SSCI). SSR is a phenomenon that can cause increased fatigue or critical failure of generator turbine shaft systems due to an energy exchange between the generator and a series-compensated transmission system, either through sustained or poorly damped oscillations, or transient effects. SSTI occur when an interaction happens between an HVDC link, FACTS device, or other power electronic controller and the mechanical mass system of a generator. The power electronic controller can exhibit negative damping at sub-synchronous frequencies, which can cause un-damped or growing oscillations in the known mechanical torsional modes of oscillation in the generator shaft system. SSCI phenomenon is a control interaction that can occur between any power electronic devices, such as wind turbine, and a series-compensated system. The oscillations resulting from SSCI may grow very quickly. Another SSCI phenomenon is the interaction between power electronic controllers such as grid-connected inverters for renewable energy sources integration. Such resonances can be both sub-synchronous and super-synchronous and may lead to inverter control instability and other dynamic problems when the connected grid is becoming weak.

The objective of this Seminar is to provide an introduction to SSI phenomena including SSR, SSTI and SSCI. To give a guide on how to identify potentiality where SSTI and/or SSCI may be an issue; how to study the phenomena; how to control, mitigate, and/or protect against any adverse effects associated with these interactions; and potential types of system changes or additions that would require equipment owners and/or operators to re-examine their system for the possibility of SSTI and/or SSCI.

Big data analytics promises to bring many benefits including increased security, safety, insight, and understanding about how to best support highly valued public services. Conversely, such analytics could be used to violate inappropriately individual privacy that could bring both immediate harm and cause long-term unintended consequences unless the data is secured and protected. With the emergence of the Internet of Things, the volume of data that will be subject to analysis will increase many times over. The nature of the data will also change because these devices will capture a wide-range of human activities that could be linked to individual behaviors through deep analytics. This talk will discuss how personal privacy is at risk now and how the introduction of the IoT complicates and expands related concerns.

Safety Instrument Systems (SIS) intend to control risk of hazards to a tolerable boundary by reducing dangerous failures rate. There are two types of failures; random and systematic. Random failures occur at random times and result from one or more degradation mechanisms. Systematic failures, however, are related to a deterministic way to a certain cause, which can only be eliminated by a modification of the design or manufacturing process, operational procedures, documentation or other relevant factors. Both systematic safety integrity (to avoid systematic failures) and hardware safety Integrity (to avoid random failures) are needed to meet the required risk reduction target for a SIS. Thus, if systematic integrity is missed, much is neglected. Studies show that many catastrophic accidents occurring in process industries address multiple systematic failures. Unlike random failures, systematic failures cannot be analyzed straightforwardly. The author’s experiences in automation field of process industries shows avoidance and control of systematic failures in SIS are not being highlighted as much as random hardware failures. This talk focuses on systematic failures and discusses procedures, techniques and measures to be used for avoidance and control of systematic failures.

There is increasing interest in understanding more about pipeline leak detection methods and how to implement them for liquid pipelines. Leak detection is a diverse topic drawing on a broad range of technical skills and experience. This presentation will provide an overview of leak detection methods focussing on their applicability for upstream and midstream liquid pipelines. Also, the latest pipeline regulatory requirements, including how leak detection fits into Pipeline Safety and Loss Management programs, will be presented. The presentation will close with a discussion of practical applications and lessons learned on recent projects.

The cloud offers near infinite storage and compute power to perform advanced analytics and machine learning, but edge processing on-site can also add value in many scenarios. Moving services typically run in the cloud such as machine learning, video/picture analysis, and stream analytics to the edge, whether it is a well site, plant, or substation can have significant benefits. Industry adoption of protocols such as MQTT and OPC UA, as well as new software gateways make connecting brownfield equipment to the cloud easier than ever. Come to learn how the Internet of Things is changing industry and new tools you can use to reduce cost, improve safety and develop new services.

AESO is the independent system operator of the Alberta power grid and market, who also plays the critical roles of Balancing Authority (BA) and Reliability Coordinator (RC) in operating the Alberta Interconnected Electric Systems (AIES). As a continuation of the speech given on Feb 1st 2018, the presentation will first briefly review the Grid and Market structures of Alberta and how AESO is equipped to balance the supply and demand in real-time (the BA role). Subsequently, the on-line Network Applications in EMS which are crucial for AESO to fulfill its role as RC will be explained in detail, including the modeling of AIES, the function of State Estimator (SE), Real-Time Contingency Analysis (RTCA), and Voltage Stability Analysis (VSA). How the market tools and EMS applications are utilized at AESO to help achieve reliability and efficiency in system operations will also be discussed.

Artificial Intelligence (AI) has recently emerged as a science even though it may still be considered in its early stages of development. Depending on the goals and methods employed in research, its definition varies. As a broad description, it may be described as the science of making machines do things that would require intelligence if done by humans.

Although AI applications are now being considered in a very wide variety of disciplines, ranging from humanities to natural and applied sciences, this seminar will focus on AI applications in electric power systems. In this context, application of artificial neural networks (ANNs) and fuzzy logic is commonly referred to in the literature as AI applications in power systems.

Over the past 25 years or so, feasibility of the application of AI for a variety of topics in power systems has been explored by a number of investigators. Topics explored vary from load forecast to real-time control and prote! ction. In addition to providing a brief introduction to ANNs and fuzzy logic, a number of examples of their application, that have been proposed, will be outlined and discussed in the seminar.

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International standards IEC 61511 and IEC 61508 provide guidance for implementing the safety system life-cycle phases. Armed with this knowledge safety design engineers may feel that they can tackle any project. However, the scope of a safety system project can vary considerably. The SIS may be part of a new multibillion dollar process plant, a facility revamp or just involve the addition of a few safety functions to an existing installation. The execution will vary considerably depending on the overall scope and makeup of the project even though the basic steps will be similar in concept.

Most importantly, the overall project schedule and resourcing is often governed by scope other than the safety system. A large project may take four to seven years from conception to startup. Essentially the safety engineer must navigate many interfaces in order to formulate a solid SIS design basis (i.e. safety requirements specification). It is important to understand! the complexity that arises from these interfaces; they need careful management.